Decentralized architecture for asynchronous sensors

This paper presents an efficient method of multi-sensor estimation that can be used with asynchronous and synchronous sensors. A decentralized archite cture is used for the fusion of information obtained from several asynchron ous measurements. The issue of the synchronization of the information, whic h is critical in the proposed method, is addressed. The information form of the Kalman filter (information filter) is used as the main algorithm for e stimation. The method is demonstrated with the implementation of a navigati on system for an autonomous land vehicle. The integrity issue is also addre ssed with the implementation of multiple independent estimation loops. The proposed method allows for efficient fusion of information obtained from di fferent measurements for covariance reduction, while providing the benefits of decentralized estimation architecture for integrity purposes. The resul ting estimates are equivalent to an optimal centralized filter when the loo ps incorporate all the information available in the system. The information obtained from each measurement is then broadcast to the other loops after being synchronized. This information is used in an assimilation stage to ac hieve more accurate estimates. The assimilation frequency is also discussed considering the trade off of fault detectability and estimation covariance reduction. The performance of the navigation method is examined by compari ng the resulting position estimates to those of independent navigation loop s.